bias (ethics/fairness)

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#fairness

1. Stereotyping, prejudice or favoritism towards some things, people, or groups over others. These biases can affect collection and interpretation of data, the design of a system, and how users interact with a system. Forms of this type of bias include:

   • automation bias
   • confirmation bias
   • experimenter’s bias
   • group attribution bias
   • implicit bias
   • in-group bias
   • out-group homogeneity bias

2. Systematic error introduced by a sampling or reporting procedure. Forms of this type of bias include:

   • coverage bias
   • non-response bias
   • participation bias
   • reporting bias = sampling bias
   • selection bias

Not to be confused with the bias term in machine learning models or prediction bias.